During the offline stage, \(\mathsf {Starmie}\)  pre-trains a column representation model that encodes columns
of data lake tables into dense high-dimensional vectors (i.e., column embeddings).
Then, we apply the trained model to all data lake tables to obtain the column embeddings via model inference.
We store the embedding vectors in efficient vector indices for online retrieval.
A key challenge for the offline stage is to train high-quality column encoders for capturing the semantics of tabular data.
In \(\mathsf {Starmie}\) , we follow a recent trend [1]}, [2]}, [3]} of
table representation learning that encodes tabular data using pre-trained language models (LMs).
Pre-trained LMs have achieved state-of-the-art performance on table understanding tasks
such as column type and relation type annotation [3]}.
However, the good performance of pre-trained LMs requires fine-tuning on high-quality labeled datasets, which are always not available in table search applications such as table union search.
Utilizing the pre-trained LMs off-the-shelf is also problematic as the column embeddings cannot capture (ir-)relevance between columns or the contextual information within tables.
To this end, in Section , we propose a contrastive learning framework for learning high-dimensional column representations in fully unsupervised manner.
We combine the framework with a multi-column table model that captures column semantics from the column values while taking the table context into account.
Then we apply the column encoder to all tables in the data lake to convert the table into a collection of embedding vectors.
